MPS 后端

mps 设备通过Metal编程框架在MacOS设备上启用高性能GPU训练。它引入了一种新的设备，用于将机器学习计算图和原语映射到高效的Metal性能着色器图框架，并分别使用Metal性能着色器框架提供的优化内核。

新的MPS后端扩展了PyTorch生态系统，并为现有脚本提供了在GPU上设置和运行操作的能力。

要开始，请将您的Tensor和Module移动到mps设备：

# Check that MPS is available
if not torch.backends.mps.is_available():
    if not torch.backends.mps.is_built():
        print("MPS not available because the current PyTorch install was not "
              "built with MPS enabled.")
    else:
        print("MPS not available because the current MacOS version is not 12.3+ "
              "and/or you do not have an MPS-enabled device on this machine.")

else:
    mps_device = torch.device("mps")

    # Create a Tensor directly on the mps device
    x = torch.ones(5, device=mps_device)
    # Or
    x = torch.ones(5, device="mps")

    # Any operation happens on the GPU
    y = x * 2

    # Move your model to mps just like any other device
    model = YourFavoriteNet()
    model.to(mps_device)

    # Now every call runs on the GPU
    pred = model(x)